Articles | Volume 26, issue 16
https://doi.org/10.5194/hess-26-4279-2022
https://doi.org/10.5194/hess-26-4279-2022
Research article
 | 
22 Aug 2022
Research article |  | 22 Aug 2022

An algorithm for deriving the topology of belowground urban stormwater networks

Taher Chegini and Hong-Yi Li

Related authors

GCAM–GLORY v1.0: representing global reservoir water storage in a multi-sector human–Earth system model
Mengqi Zhao, Thomas B. Wild, Neal T. Graham, Son H. Kim, Matthew Binsted, A. F. M. Kamal Chowdhury, Siwa Msangi, Pralit L. Patel, Chris R. Vernon, Hassan Niazi, Hong-Yi Li, and Guta W. Abeshu
Geosci. Model Dev., 17, 5587–5617, https://doi.org/10.5194/gmd-17-5587-2024,https://doi.org/10.5194/gmd-17-5587-2024, 2024
Short summary
Causal relationships between vegetation productivity, water availability, and atmospheric dryness at the catchment scale
Guta Wakbulcho Abeshu, Hong-Yi Li, Mingjie Shi, and Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2024-1748,https://doi.org/10.5194/egusphere-2024-1748, 2024
Short summary
Deriving a Transformation Rate Map of Dissolved Organic Carbon over the Contiguous U.S.
Lingbo Li, Hong-Yi Li, Guta Abeshu, Jinyun Tang, L. Ruby Leung, Chang Liao, Zeli Tan, Hanqin Tian, Peter Thornton, and Xiaojuan Yang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-43,https://doi.org/10.5194/essd-2024-43, 2024
Preprint under review for ESSD
Short summary
Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024,https://doi.org/10.5194/gmd-17-1197-2024, 2024
Short summary
Enhancing the representation of water management in global hydrological models
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023,https://doi.org/10.5194/gmd-16-5449-2023, 2023
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
To what extent do flood-inducing storm events change future flood hazards?
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024,https://doi.org/10.5194/hess-28-3161-2024, 2024
Short summary
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024,https://doi.org/10.5194/hess-28-3051-2024, 2024
Short summary
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024,https://doi.org/10.5194/hess-28-2969-2024, 2024
Short summary
Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869, https://doi.org/10.5194/hess-28-2849-2024,https://doi.org/10.5194/hess-28-2849-2024, 2024
Short summary
Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024,https://doi.org/10.5194/hess-28-2809-2024, 2024
Short summary

Cited articles

Ajaaj, A. A., Mishra, A. K., and Khan, A. A.: Urban and peri-urban precipitation and air temperature trends in mega cities of the world using multiple trend analysis methods, Theor. Appl. Climatol., 132, 403–418, https://doi.org/10.1007/s00704-017-2096-7, 2017. a
Boeing, G.: OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks, Comput. Environ. Urban, 65, 126–139, https://doi.org/10.1016/j.compenvurbsys.2017.05.004, 2017. a
Brandes, U.: A faster algorithm for betweenness centrality, J. Math. Sociol., 25, 163–177, https://doi.org/10.1080/0022250x.2001.9990249, 2001. a
Brown, S. A., Schall, J. D., Morris, J. L., Doherty, C. L., Stein, S. M., and Warner, J. C.: Urban Drainage Design Manual, Hydraulic Engineering Circular 22, Third Edition, Federal Highway Administration Press, Publication No. FHWA-NHI-10-009, 2013. a, b, c, d, e
Chegini, T., Li, H.-Y., and Leung, L. R.: HyRiver: Hydroclimate Data Retriever, Journal of Open Source Software, 6, 3175, https://doi.org/10.21105/joss.03175, 2021. a, b, c
Download
Short summary
Belowground urban stormwater networks (BUSNs) play a critical and irreplaceable role in preventing or mitigating urban floods. However, they are often not available for urban flood modeling at regional or larger scales. We develop a novel algorithm to estimate existing BUSNs using ubiquitously available aboveground data at large scales based on graph theory. The algorithm has been validated in different urban areas; thus, it is well transferable.